Deubiquitinases in muscle physiology and disorders
Bibliographic record
Abstract
In vivo, muscle and neuronal cells are post-mitotic, and their function is predominantly regulated by proteostasis, a multilayer molecular process that maintains a delicate balance of protein homeostasis. The ubiquitin-proteasome system (UPS) is a key regulator of proteostasis. A dysfunctional UPS is a hallmark of muscle ageing and is often impacted in neuromuscular disorders (NMDs). Malfunction of the UPS often results in aberrant protein accumulation which can lead to protein aggregation and/or mis-localization affecting its function. Deubiquitinating enzymes (DUBs) are key players in the UPS, controlling protein turnover and maintaining the free ubiquitin pool. Several mutations in DUB encoding genes are linked to human NMDs, such as ATXN3, OTUD7A, UCHL1 and USP14, whilst other NMDs are associated with dysregulation of DUB expression. USP5, USP9X and USP14 are implicated in synaptic transmission and remodeling at the neuromuscular junction. Mice lacking USP19 show increased maintenance of lean muscle mass. In this review, we highlight the involvement of DUBs in muscle physiology and NMDs, particularly in processes affecting muscle regeneration, degeneration and inflammation following muscle injury. DUBs have recently garnered much respect as promising drug targets, and their roles in muscle maturation, regeneration and degeneration may provide the framework for novel therapeutics to treat muscular disorders including NMDs, sarcopenia and cachexia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".